1. Field of the Invention
This invention relates to offline signature verification and more particularly to using the high pressure regions of offline signature images for signature verification.
2. Background
The need to ensure that only the right people are given authorization to access protected resources has led to the development of systems to verify personal identities. Signatures, fingerprints, palm prints, voice, and handwriting have all been used to verify the declared identity of an individual. Among all, the signature has a fundamental advantage in that it is the customary way of identifying an individual in daily operations such as automated banking transactions, electronic fund transfers, document analysis, and access control.
Signature verification systems are generally based on pattern recognition techniques. Three popular categories of such recognition techniques are statistical techniques, template matching techniques, and structural techniques. Within the statistical techniques category, there are subcatagories such as minimum distance classifiers, neural networks, and Bayesian classifiers. Minimum distance classifiers includes simple distance classifiers (SDCs) like the Euclidean or Mahalanobis distance, or Hidden Markov Models (HMMs). All of these verification techniques are thoroughly described in the prior art and are well known to those with skill in the relevant art.
A signature verification system should be able to screen and reject signatures presenting “inter-class variance” which signifies that the signature does not belong to the authorized individual. At the same time, such systems should minimize the rejections based on variations between genuine signatures from the authorized individual (“intra-class variance”).
Signature verification systems can be classified into offline and online categories. In offline systems, a completed writing is digitized using a handheld or flatbed scanner and the signature is stored as an image. These images are referred to as static signatures and are generally stored in a two dimension, grayscale image format. Offline systems are of interest in scenarios where only hard copies of signatures are available, for example where a large number of documents (such as personal checks) need to be authenticated.
For online verification systems, a special pen can be used on an electronic surface such as a digitizer combined with a liquid crystal display (LCD). Apart from the two-dimensional coordinates of the successive points of the writing, pen pressure, as well as the angle and direction of the pen, are captured dynamically and then stored as a function of time. The stored data is referred to as a dynamic signature and can also contain information on pen velocity and acceleration. Online systems are of special interest for “point-of-sale” and security applications.
Since online signatures contain dynamic information in addition to the final signature image, they are substantially more difficult to forge. Offline systems, by contrast, provide only static images that may have been created by a forger with substantial time and resources. Additionally, static signature images tend to include background noise, variations in stroke-width, and other image quality problems. It is therefore commonly recognized that offline signature verification systems are much less reliable than online systems.
The problem of offline signature verification has been further complicated by the existence of three different types of forgeries: “random forgeries” which are produced without knowing either the name of the signer nor the shape of his signature; “simple forgeries” which are produced by those who know the name of the signer but do not have an example of a genuine signature; and “skilled forgeries” which are produced by those who have access to an a genuine signature (or a copy of it) and attempt to imitate it as closely as possible. It is obvious that the problem of signature verification becomes more and more difficult when passing from random to simple and skilled forgeries, the latter being so difficult a task that even human beings make errors in several cases. In fact, exercises in imitating a signature often result in the production of forgeries so similar to the originals that discrimination is practically impossible. And in many cases, the “inter-class variance” is harder to identify because there is a large variability introduced by some signers when writing their own signatures (i.e., intra-class variance).
It is also important to note that most of the signature verification systems that have been proposed, while performing reasonably well on a single category of forgeries (random, simple or skilled), decrease in performance when working with all the categories of forgeries simultaneously, and generally this decrement is bigger than one would expect. The main reason for this behavior lies in the difficulty of defining a verification technique that is adequate to work with all the classes of forgery simultaneously. Also, in current verification systems, it is difficult to find clear and strong justifications of why a specific set of features is used instead of others. Even if a justification is found, extracting such features can be difficult and not robust.
Verification techniques can be implemented in different ways. For example, when a technique extracts and analyzes particular features of the signature with respect to the entire image, the subsequent analysis is said to analyze the “global” features. But the signature image can also be divided into multiple pieces or partitions and the signature's unique features in each partition can then be extracted and analyzed separately using one or more of the available verification techniques, thereby analyzing the signature's “local” features. The prior art describes different verification methodologies that can be applied to the global features, to the local features, or to both. In many instances, the global features of a signature are analyzed under one technique, and the local features are analyzed under another.
Due to the complexity of offline signature verification and shortcomings in the known verification techniques, current authentication methodologies fail to achieve the optimal levels of accuracy and there remains a need for new methods of analyzing signature images that can improve the accuracy of offline signature verification.